• dplyr was used to manipulate the data
• ggplo2 was used to create figures
• tidyr was not used since the data was already arranged to desired standards

library(dplyr)
library(ggplot2)

• Mantis shrimps or stomatopods
• Marine crustaceans of the order Stomatopoda
• Some species have blunt forelimbs while others have sharp ones

• Appendages
• Telson

• During telson sparring, mantis shrimp take turns striking with their appendages against the telson of their opponent
• Individuals direct nearly every strike to the telson, and this is facilitated by the recipient assuming a coiled position referred to as a ‘telson coil’

data_1 <- read.csv("https://ndownloader.figshare.com/files/16851587")
knitr::kable(data_1[1:5,1:3])

Mantis Shrimp Shells

• Many types of animals develop morphological traits to partake in ritualized combat.
• Mantis shrimp present an interesting case in which their unique form of ritualized combat, termed telson sparring, may have coevolved with specialized morphological armour.

• The coefficient of restitution (COR), also denoted by (e), is the ratio of the final to initial relative velocity between two objects after they collide

• can also be viewed in terms of kinetic energy before and after a collision.

• e is usually a positive number between 0 and 1:

• e = 0: This is a perfectly inelastic collision. Kinetic energy is dissipated (converted to heat or work).

• 0 < e < 1: This is a real-world inelastic collision, in which some kinetic energy is dissipated.

• e = 1: This is a perfectly elastic collision, in which no kinetic energy is dissipated, and the objects rebound from one another with the same initial velocity.

• separated species with "smasher" and "spearer" appendages by using filter()
• Wanted to see if there were different trends between appendage types

smasher_data <- data_1 %>%
  group_by(appendage) %>%
  filter(appendage == "smasher") 

spearer_data <- data_1 %>%
  group_by(appendage) %>%
  filter(appendage != "smasher")

lm_1 <- lm(cor.telson~cor.abdomen, data_1) #linear model function 
summary(lm_1)[[9]] #number assocaited with chunk, returns r squared value

## [1] 0.1009381

results <- summary(lm_1) 
results$coefficients[8] #returns p value

## [1] 0.001330482